عنوان مقاله [English]
Most techniques and methods in the agricultural planning and management based on the climate variation and climate change are implicitly or explicitly dependent on the hydrocliamtological estimations. Hydroclimatolgical estimation is actually the determination and warning of specific situations in a certain time and place such as drought and floods. Further improvements in the modeling for those estimations are up to the growing knowledge on the interaction between climatic and hydrological events as well as developing new models and improvement of existing ones. Presenting a hydrocliamtological model to be able to map the nonlinear relationship between climate signals and hydrological models; to model the non stationary time series; providing probabilistic estimation could be a significant contribution. A model is presented in this paper based on the correlation analysis and statistical modeling which is compared with a conventional nonparametric model (nearest neighborhood) in point and interval estimation. One of the important challenges in hydrocliamtological estimation is to calculate estimation uncertainty in which the proposed method deals with it. The contribution of this paper is to provide a simple but efficient method for hydrocliamtological estimation in an uncertain environment. The case study of this research is forecasting of streamflow of Zayandeh-rud River in drought and normal situations. The results demonstrate the supremacy of the proposed model in comparison with the conventional models.